{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:09:26Z","timestamp":1775146166421,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>A non-communicable disease that affects the bones is called osteoporosis (OP). It results in altered bone microstructures, insufficient bone regeneration, and decreased bone mineral density (BMD). Until the disease progresses, individuals often don't realize they have it. Even with the widespread use of deep learning (DL) and machine learning (ML) algorithms, the early diagnosis of osteoporosis patients can still be improved. By automatically extracting hierarchical features from X-ray images through the application of a wide range of competitive ML (Gaussian Process (GP) kernel, SVM, XGBoost, Bagging), and DL models, we provide highly accurate and consistent predictions to accurately and early predict osteoporosis patients. In the proposed approach, we extracted and integrated significant features using three image feature extractors (LBP, CLBP, and HOG) with 95% PCA. Furthermore, we proposed upscaling (2\u2009\u00d7\u2009&amp;4\u2009\u00d7) employing bicubic interpolation to enhance the image intensity. Subsequently, we applied the interpolated images to the suggested customized convolution neural network (CNN) model to improve the diagnostic performance. The proposed GP model outperformed other GP kernel and traditional ML models, achieving notable results with 67.74% accuracy, 61.97% precision, 92.52% recall, 74.12% F1-score, and a 67.77% AUC. To enhance osteoporosis classification, we developed a hybrid stacked CNN model, which demonstrated excellent performance on interpolated images. The proposed CNN model achieved state-of-the-art results, with 98% accuracy on the training set, and 95% accuracy on both the test and validation sets, significantly surpassing existing models. The numerical comparison shows the CNN model's superiority, with about a 30% increase in accuracy over the GP model. However, this research demonstrates the strong efficacy of the CNN model in handling complicated image data, making it a more sophisticated tool for osteoporosis detection.<\/jats:p>","DOI":"10.1007\/s44230-025-00102-9","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T08:09:26Z","timestamp":1748765366000},"page":"196-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1145-3385","authenticated-orcid":false,"given":"Ruhul","family":"Amin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3699-0494","authenticated-orcid":false,"given":"Md.Shamim","family":"Reza","sequence":"additional","affiliation":[]},{"given":"Dewan Ahmed","family":"Muhtasim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-2468","authenticated-orcid":false,"given":"Jungpil","family":"Shin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-8071","authenticated-orcid":false,"given":"Md.","family":"Maniruzzaman","sequence":"additional","affiliation":[]},{"given":"Md.Mahfujul","family":"Hasan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,1]]},"reference":[{"issue":"8","key":"102_CR1","doi-asserted-by":"publisher","first-page":"E2121106","DOI":"10.1001\/jamanetworkopen.2021.21106","volume":"4","author":"L Wang","year":"2021","unstructured":"Wang L, et al. Prevalence of osteoporosis and fracture in China: the china osteoporosis prevalence study. JAMA Netw Open. 2021;4(8):E2121106. https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.21106.","journal-title":"JAMA Netw Open"},{"issue":"9","key":"102_CR2","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s12603-023-1971-4","volume":"27","author":"Z Zhu","year":"2023","unstructured":"Zhu Z, et al. Sex specific global burden of osteoporosis in 204 countries and territories, from 1990 to 2030: an age-period-cohort modeling study. J Nutr Heal Aging. 2023;27(9):767\u201374. https:\/\/doi.org\/10.1007\/s12603-023-1971-4.","journal-title":"J Nutr Heal Aging"},{"issue":"May","key":"102_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fendo.2022.882241","volume":"13","author":"Y Shen","year":"2022","unstructured":"Shen Y, et al. The global burden of osteoporosis, low bone mass, and its related fracture in 204 countries and territories, 1990\u20132019. Front Endocrinol (Lausanne). 2022;13(May):1\u201311. https:\/\/doi.org\/10.3389\/fendo.2022.882241.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"102_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/nu12010030","volume":"12","author":"K Schraders","year":"2020","unstructured":"Schraders K, et al. Correction: quantitative ultrasound and dual x-ray absorptiometry as indicators of bone mineral density in young women and nutritional factors affecting it. (Nutrients, (2019), 11(10), 2336, DOI: 10.3390\/nu11102336). Nutrients. 2020;12(1):1\u201311. https:\/\/doi.org\/10.3390\/nu12010030.","journal-title":"Nutrients"},{"issue":"4","key":"102_CR5","doi-asserted-by":"publisher","first-page":"187","DOI":"10.3390\/osteology1040018","volume":"1","author":"M Ali","year":"2021","unstructured":"Ali M, Uddin Z, Hossain A. Prevalence and patterns of risk of osteoporosis in bangladeshi adult population: an analysis of calcaneus quantitative ultrasound measurements. Osteology. 2021;1(4):187\u201396. https:\/\/doi.org\/10.3390\/osteology1040018.","journal-title":"Osteology"},{"issue":"2","key":"102_CR6","first-page":"92","volume":"43","author":"KN Tu","year":"2018","unstructured":"Tu KN, et al. Osteoporosis: a review of treatment options. P T. 2018;43(2):92\u2013104.","journal-title":"P T"},{"issue":"1","key":"102_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13673-020-00220-2","volume":"10","author":"DH Lee","year":"2020","unstructured":"Lee DH, Li Y, Shin BS. Generalization of intensity distribution of medical images using GANs. Human-Centric Comput Inf Sci. 2020;10(1):1\u201315. https:\/\/doi.org\/10.1186\/s13673-020-00220-2.","journal-title":"Human-Centric Comput Inf Sci"},{"key":"102_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/395915","volume":"2013","author":"AP Mahmoudzadeh","year":"2013","unstructured":"Mahmoudzadeh AP, Kashou NH. Evaluation of interpolation effects on upsampling and accuracy of cost functions-based optimized automatic image registration. Int J Biomed Imaging. 2013;2013:1\u201319. https:\/\/doi.org\/10.1155\/2013\/395915.","journal-title":"Int J Biomed Imaging"},{"issue":"3","key":"102_CR9","first-page":"1","volume":"6","author":"S Yuvaraj","year":"2020","unstructured":"Yuvaraj S, Seshasayanan R, Senthil Kumar KK. Wavelet based various interpolation techniques for high resolution image enhancement processing. 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As such, ethical approval is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}